Rethinking deep learning: linear regression remains a key benchmark in predicting terrestrial water storage
Wanshu Nie, Sujay V. Kumar, Junyu Chen, Long Zhao, Olya Skulovich, Jinwoong Yoo, Justin Pflug, Shahryar Khalique Ahmad, Goutam Konapala
机器学习的最新进展,如长短期记忆(LSTM)模型和变形金刚,已被广泛应用于水文应用,在深度学习模型中表现出令人印象深刻的表现,并在各种任务中超越物理模型。 然而,他们在预测陆地表面状态(如陆地水储存(TWS)方面的优势仍然不清楚,这些状态由自然变化和人类驱动的修改等许多因素主导。 在这里,使用开放获取,具有全球代表性的HydroGlobe数据集 - 包含仅来自陆地表面模型模拟的基线版本和包含多源遥感数据同化的高级版本 - 我们表明线性回归是一个稳健的基准,优于更复杂的LSTM和Temporal Fusion Transformer for TWS预测。 我们的研究结果强调了在开发和评估深度学习模型时将传统统计模型作为基准的重要性。 此外,我们强调建立具有全球代表性的基准数据集的迫切需要,这些数据集可以捕获自然变异和人类干预的综合影响。
Recent advances in machine learning such as Long Short-Term Memory (LSTM) models and Transformers have been widely adopted in hydrological applications, demonstrating impressive performance amongst deep learning models and outperforming physical models in various tasks. However, their superiority in predicting land surface states such as terrestrial water storage (TWS) that are dominated by many factors such as natural variability and human driven modifications remains unclear. Here, using the o...
